Source code for airflow.providers.common.ai.operators.llm
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"""Operator for general-purpose LLM calls."""
from __future__ import annotations
from collections.abc import Sequence
from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Any
from pydantic import BaseModel
from airflow.providers.common.ai.hooks.pydantic_ai import PydanticAIHook
from airflow.providers.common.ai.mixins.approval import LLMApprovalMixin
from airflow.providers.common.ai.utils.logging import log_run_summary
from airflow.providers.common.compat.sdk import BaseOperator
if TYPE_CHECKING:
from pydantic_ai import Agent
from airflow.sdk import Context
[docs]
class LLMOperator(BaseOperator, LLMApprovalMixin):
"""
Call an LLM with a prompt and return the output.
Uses a :class:`~airflow.providers.common.ai.hooks.pydantic_ai.PydanticAIHook`
for LLM access. Supports plain string output (default) and structured output
via a Pydantic ``BaseModel``. When ``output_type`` is a ``BaseModel`` subclass,
the result is serialized via ``model_dump()`` for XCom.
:param prompt: The prompt to send to the LLM.
:param llm_conn_id: Connection ID for the LLM provider.
:param model_id: Model identifier (e.g. ``"openai:gpt-5"``).
Overrides the model stored in the connection's extra field.
:param system_prompt: System-level instructions for the LLM agent.
:param output_type: Expected output type. Default ``str``. Set to a Pydantic
``BaseModel`` subclass for structured output.
:param agent_params: Additional keyword arguments passed to the pydantic-ai
``Agent`` constructor (e.g. ``retries``, ``model_settings``, ``tools``).
See `pydantic-ai Agent docs <https://ai.pydantic.dev/api/agent/>`__
for the full list.
:param require_approval: If ``True``, the task defers after generating
output and waits for a human reviewer to approve or reject via the
HITL interface. Default ``False``.
:param approval_timeout: Maximum time to wait for a review. When
exceeded, the task fails with ``TimeoutError``.
:param allow_modifications: If ``True``, the reviewer can edit the output
before approving. The modified value is returned as the task result.
Default ``False``.
"""
[docs]
template_fields: Sequence[str] = (
"prompt",
"llm_conn_id",
"model_id",
"system_prompt",
"agent_params",
)
def __init__(
self,
*,
prompt: str,
llm_conn_id: str,
model_id: str | None = None,
system_prompt: str = "",
output_type: type = str,
agent_params: dict[str, Any] | None = None,
require_approval: bool = False,
approval_timeout: timedelta | None = None,
allow_modifications: bool = False,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
[docs]
self.llm_conn_id = llm_conn_id
[docs]
self.model_id = model_id
[docs]
self.system_prompt = system_prompt
[docs]
self.output_type = output_type
[docs]
self.agent_params = agent_params or {}
[docs]
self.require_approval = require_approval
[docs]
self.approval_timeout = approval_timeout
[docs]
self.allow_modifications = allow_modifications
@cached_property
[docs]
def llm_hook(self) -> PydanticAIHook:
"""Return PydanticAIHook for the configured LLM connection."""
return PydanticAIHook(llm_conn_id=self.llm_conn_id, model_id=self.model_id)
[docs]
def execute(self, context: Context) -> Any:
agent: Agent[None, Any] = self.llm_hook.create_agent(
output_type=self.output_type, instructions=self.system_prompt, **self.agent_params
)
result = agent.run_sync(self.prompt)
log_run_summary(self.log, result)
output = result.output
if self.require_approval:
self.defer_for_approval(context, output) # type: ignore[misc]
if isinstance(output, BaseModel):
output = output.model_dump()
return output